Azure Data Factory libraries for Python

Compose data storage, movement, and processing services into automated data pipelines with Azure Data Factory

Learn more about Data Factory and get started with the Create a data factory and pipeline using Python quickstart.

Management module

Create and manage Data Factory instances in your subscription with the management module.

Installation

Install the package with pip:

pip install azure-mgmt-datafactory 

Example

Create a Data Factory in your subscription on the East US region.

from azure.common.credentials import ServicePrincipalCredentials
from azure.mgmt.resource import ResourceManagementClient
from azure.mgmt.datafactory import DataFactoryManagementClient
from azure.mgmt.datafactory.models import *
import time

#Create a data factory
subscription_id = '<Specify your Azure Subscription ID>'
credentials = ServicePrincipalCredentials(client_id='<Active Directory application/client ID>', secret='<client secret>', tenant='<Active Directory tenant ID>')
adf_client = DataFactoryManagementClient(credentials, subscription_id)

rg_params = {'___location':'eastus'}
df_params = {'___location':'eastus'}  

df_resource = Factory(___location='eastus')
df = adf_client.factories.create_or_update(rg_name, df_name, df_resource)
print_item(df)
while df.provisioning_state != 'Succeeded':
    df = adf_client.factories.get(rg_name, df_name)
    time.sleep(1)